This section provides background information related to the present disclosure which is not necessarily prior art.
Along with increasingly enriched information provided by Internet, more and more objectionable information arises. The objectionable information such as indecent images, e.g. erotic images and the like, corrupts society value and is harmful to physical and mental health of adolescents. Thus, recognition and interception of such images has become a crucial task of Internet content filtering.
Since most indecent images depicts massive naked human skin and meanwhile detecting naked human skin in an image is much easier than directly judging whether an image is an indecent image, naked human skin detection is an effective heuristic method for indecent image recognition. Through naked human skin detection, highly possible indecent images can be detected and then examined by man, so that manpower examination workload can be remarkably reduced.
One of existing naked human skin detection methods is based on skin-color detection and human body area shape features. According to the method, skin-color or near-skin-color areas in an image to be examined is detected through skin-color detection, then area shape features of the areas are extracted for differentiating human-skin areas from non-human-skin background areas, and after that, a trained classifier makes a judgment.
Existing skin-color detection is mainly based on statistical probability distribution of human skin color. The Bayesian decision method is one of the widely-used skin-color detection methods. According to the method, distributions of skin-color and non-skin-color in a large sample set are calculated. For a given color, a posterior probability that the color is skin color is calculated using the Bayesian equation according to the two distributions. The value of the posterior probability determines whether the area is a skin-color area or a non-skin-color area.
The commonly-used human body area shape features mainly include: an area ratio of a skin-color area to an image area (the skin-color area refers to the area composed of each skin-color pixel, and is not necessarily continuous); an area ratio of the largest skin Blob to the image area (the skin Blob refers to connected area composed of skin-color pixels); the amount of skin Blobs; an area ratio of a skin Blob to a circumscribed rectangular (or a convex hull); the semi-axis length, the eccentricity, the direction and etc. of an equivalent ellipse of the skin Blob; the moment invariant of the skin-color area; and the area of a human face, etc.
Those area shape features are extracted from a set of training images to train a classifier for automatic classification of indecent images and normal images. The training image set includes a positive-example sample set (i.e., composed of indecent images) and a negative-example sample set (i.e., composed of normal images). Features extracted from each sample set are marked with a label of the sample set before used for training the classifier. Classifiers applicable for this purpose mainly include Support Vector Machine (SVM), Multi-Layer Perception (MLP) network, decision tree, etc.
In the priori art, different types of negative-example images are collected to form a negative-example sample set, thus distribution of certain area shape features of the negative-example images becomes more scattered, which enlarges the overlap between features extracted from the positive-example images and features extracted from the negative-example images. For example, many overlapping features of a portrait image from the negative-example images and the indecent images are mandatorily marked with different labels, thus the trained classifier will be over-fitted and the classification plane is distorted, which results in an increased detection error rate of erroneously detected portrait images and an increased detection missing rate for not detected indecent images, as well as an unpredictable impact on the classification result of scene images. Therefore, the trained classifier of the priori art faces problems of high detection missing rate and high detection error rate.